Closed-World object detection.
Much effort has been spent on supervised object detection with a closed world assumption [1]}, [2]}, [3]}, [4]}, [5]}, [6]}.
The ability to detect known objects has been improving with better architecture designs [7]}, [8]}, [9]} or objectives [10]}.
Also, localizing objects given a few training examples or semantic information is becoming a popular research topic [11]}, [12]}. However, these attempts are still constrained by the taxonomy defined by the dataset. Our model can detect more categories than defined by the dataset, which can be very useful in few-shot or zero-shot object detection.
